Towards an assistive and pattern learning-driven process modeling approach
Loading...
Author (Corporation)
Publication date
2019
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editors
Editor (Corporation)
Supervisor
Parent work
Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)
Special issue
DOI of the original publication
Series
Series number
Volume
Issue / Number
Pages / Duration
Patent number
Publisher / Publishing institution
Place of publication / Event location
Palo Alto
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge, we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.
Keywords
Subject (DDC)
330 - Wirtschaft
Event
AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)
Exhibition start date
Exhibition end date
Conference start date
Conference end date
Date of the last check
ISBN
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Diamond
License
Citation
LAURENZI, Emanuele, Knut HINKELMANN, Stephan JÜNGLING, Devid MONTECCHIARI, Charuta PANDE und Andreas MARTIN, 2019. Towards an assistive and pattern learning-driven process modeling approach. In: Andreas MARTIN, Knut HINKELMANN, Aurona GERBER, Doug LENAT, Frank VAN HARMELEN und Peter CLARK (Hrsg.), Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). Palo Alto. 2019. Verfügbar unter: https://doi.org/10.26041/fhnw-6431